Format

Send to

Choose Destination
Med Care. 2019 Jul;57(7):560-566. doi: 10.1097/MLR.0000000000001140.

Training and Interpreting Machine Learning Algorithms to Evaluate Fall Risk After Emergency Department Visits.

Author information

1
BerbeeWalsh Department of Emergency Medicine, University of Wisconsin School of Medicine and Public Health.
2
Health Innovation Program.
3
Department of Computer Sciences, University of Wisconsin-Madison.
4
Departments of Population Health Sciences.
5
Family Medicine.
6
Biostatistics and Medical Informatics.
7
Pediatrics, University of Wisconsin School of Medicine and Public Health.
8
Department of Medicine, Division of Geriatrics and Gerontology, University of Wisconsin School of Medicine and Public Health, Madison, WI.

Abstract

BACKGROUND:

Machine learning is increasingly used for risk stratification in health care. Achieving accurate predictive models do not improve outcomes if they cannot be translated into efficacious intervention. Here we examine the potential utility of automated risk stratification and referral intervention to screen older adults for fall risk after emergency department (ED) visits.

OBJECTIVE:

This study evaluated several machine learning methodologies for the creation of a risk stratification algorithm using electronic health record data and estimated the effects of a resultant intervention based on algorithm performance in test data.

METHODS:

Data available at the time of ED discharge were retrospectively collected and separated into training and test datasets. Algorithms were developed to predict the outcome of a return visit for fall within 6 months of an ED index visit. Models included random forests, AdaBoost, and regression-based methods. We evaluated models both by the area under the receiver operating characteristic (ROC) curve, also referred to as area under the curve (AUC), and by projected clinical impact, estimating number needed to treat (NNT) and referrals per week for a fall risk intervention.

RESULTS:

The random forest model achieved an AUC of 0.78, with slightly lower performance in regression-based models. Algorithms with similar performance, when evaluated by AUC, differed when placed into a clinical context with the defined task of estimated NNT in a real-world scenario.

CONCLUSION:

The ability to translate the results of our analysis to the potential tradeoff between referral numbers and NNT offers decisionmakers the ability to envision the effects of a proposed intervention before implementation.

PMID:
31157707
PMCID:
PMC6590914
[Available on 2020-07-01]
DOI:
10.1097/MLR.0000000000001140

Supplemental Content

Full text links

Icon for Wolters Kluwer
Loading ...
Support Center